code: def microbenchmark(
      operator,
      args,
      kwargs,
      dtype,
      accuracy_checking,
      repeats,
      measure_nvfuser,
      device,
  ):
      gm, gm_args = gen_gm_and_inputs(operator, args, kwargs)
      torch.jit._builtins._register_builtin(
          torch.ops.aten.convolution_backward.default,
          "aten::convolution_backward",
      )
      if device == "cuda":
          cudagraphs_eager = cudagraphs_inner(
              gm, gm_args, copy_outputs=False, copy_inputs=False
          )
          compiled_fn = compile_fx(gm, gm_args)
          cudagraphs_compiled = cudagraphs_inner(
              compiled_fn,
              gm_args,
              copy_outputs=False,
              copy_inputs=False,
          )
          compiled = [cudagraphs_eager, cudagraphs_compiled]
      else:
          compiled_fn = compile_fx(gm, gm_args)
          compiled = [gm, compiled_fn]
      if measure_nvfuser:
          g = convert_to_jit(gm, gm_args)
          cudagraphs_jit = cudagraphs_inner(
              g, gm_args, copy_outputs=False, copy_inputs=False
          )
          compiled += [cudagraphs_jit]
      if accuracy_checking:
          repeats = 1
  
      medians = compute_speedups(
          operator,
          compiled,
          gm_args,
          repeats,
          accuracy_checking,
          device,
      )
      return medians
  
file_path: pytorch/benchmarks/dynamo/microbenchmarks/operatorbench.py
imports: import click
  import numpy
  import torch
  from operator_inp_utils import OperatorInputsLoader
  from torch._dynamo.backends.cudagraphs import cudagraphs_inner
  from torch._dynamo.testing import same
  from torch._inductor.compile_fx import compile_fx
  from torch._inductor.decomposition import decompositions
  from torch._inductor.lowering import lowerings
  from torch._inductor.utils import gen_gm_and_inputs
  from torch.utils._pytree import tree_map_only
label_desc: Use image node_id 5 for calling a global function with example usage: microbenchmark(operator, args, kwargs, dtype, accuracy_checking, repeats, measure_nvfuser, device) and returns: medians